Synergy analysis of agricultural economic cycle fluctuation based on ant colony algorithm

The measurement of the agricultural economic cycle and its synergy analysis are of great significance for the formulation of agricultural macroeconomic policies. When the traditional methods are used to analyze the main factors affecting the synergy of agricultural economic cycle fluctuations, almos...

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Main Authors: Cheng Shen, A. López Miguel
Format: Article
Language:English
Published: De Gruyter 2018-12-01
Series:Open Physics
Subjects:
Online Access:https://doi.org/10.1515/phys-2018-0119
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author Cheng Shen
A. López Miguel
author_facet Cheng Shen
A. López Miguel
author_sort Cheng Shen
collection DOAJ
description The measurement of the agricultural economic cycle and its synergy analysis are of great significance for the formulation of agricultural macroeconomic policies. When the traditional methods are used to analyze the main factors affecting the synergy of agricultural economic cycle fluctuations, almost all of them use the correlation coefficient or the degree of agreement to express the degree of economic cycle synergy. It is impossible to accurately evaluate the degree of synergy of economic cycle fluctuations. To solve this problem, a quantitative calculation model of agricultural economic cycle synergy evaluation based on the ant colony algorithm is proposed. On the basis of the collected monthly data of agricultural economic operation indicators, a prosperity index that comprehensively reflects the development of agricultural economy is constructed. The law of economic prosperity cycle fluctuation is analyzed according to economic prosperity index, and HP filtering is utilized to decompose the trend fluctuations of the economic time series of various industries and obtain the fluctuation components. The ant colony algorithm is utilized to optimize the spearman correlation analysis method, and the correlation analysis of the wave components is carried out. The Fisher-z conversion is performed on the optimal Spearman correlation coefficient obtained by the optimization. The transformed results represent the degree of economic cycle synergy, considering the geographical distance, economic space spillover, fiscal policy synergy, regional income gap and geographical neighbors, a quantitative calculation model for agricultural economic cycle synergy evaluation is established. The results show that the agricultural economic cycle has a certain spatial correlation, and will expand the agricultural economic fluctuations and form the cycle synergy through the periodic space overflow.
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spelling doaj.art-9b25b73773bd4205832b3a19351602952022-12-21T18:37:35ZengDe GruyterOpen Physics2391-54712018-12-0116197898810.1515/phys-2018-0119phys-2018-0119Synergy analysis of agricultural economic cycle fluctuation based on ant colony algorithmCheng Shen0A. López Miguel1College of Economics and Management, China Agricultural University, Beijing100083, ChinaDepartment of Mathematics, University of Castilla-La Mancha, Campus of Cuenca, 16071-Cuenca, Castilla-La Mancha, SpainThe measurement of the agricultural economic cycle and its synergy analysis are of great significance for the formulation of agricultural macroeconomic policies. When the traditional methods are used to analyze the main factors affecting the synergy of agricultural economic cycle fluctuations, almost all of them use the correlation coefficient or the degree of agreement to express the degree of economic cycle synergy. It is impossible to accurately evaluate the degree of synergy of economic cycle fluctuations. To solve this problem, a quantitative calculation model of agricultural economic cycle synergy evaluation based on the ant colony algorithm is proposed. On the basis of the collected monthly data of agricultural economic operation indicators, a prosperity index that comprehensively reflects the development of agricultural economy is constructed. The law of economic prosperity cycle fluctuation is analyzed according to economic prosperity index, and HP filtering is utilized to decompose the trend fluctuations of the economic time series of various industries and obtain the fluctuation components. The ant colony algorithm is utilized to optimize the spearman correlation analysis method, and the correlation analysis of the wave components is carried out. The Fisher-z conversion is performed on the optimal Spearman correlation coefficient obtained by the optimization. The transformed results represent the degree of economic cycle synergy, considering the geographical distance, economic space spillover, fiscal policy synergy, regional income gap and geographical neighbors, a quantitative calculation model for agricultural economic cycle synergy evaluation is established. The results show that the agricultural economic cycle has a certain spatial correlation, and will expand the agricultural economic fluctuations and form the cycle synergy through the periodic space overflow.https://doi.org/10.1515/phys-2018-0119ant colony algorithmagricultureeconomic cycle fluctuationsynergy analysisspearman correlation analysis method82.20.wt88.05.lg88.05.qr
spellingShingle Cheng Shen
A. López Miguel
Synergy analysis of agricultural economic cycle fluctuation based on ant colony algorithm
Open Physics
ant colony algorithm
agriculture
economic cycle fluctuation
synergy analysis
spearman correlation analysis method
82.20.wt
88.05.lg
88.05.qr
title Synergy analysis of agricultural economic cycle fluctuation based on ant colony algorithm
title_full Synergy analysis of agricultural economic cycle fluctuation based on ant colony algorithm
title_fullStr Synergy analysis of agricultural economic cycle fluctuation based on ant colony algorithm
title_full_unstemmed Synergy analysis of agricultural economic cycle fluctuation based on ant colony algorithm
title_short Synergy analysis of agricultural economic cycle fluctuation based on ant colony algorithm
title_sort synergy analysis of agricultural economic cycle fluctuation based on ant colony algorithm
topic ant colony algorithm
agriculture
economic cycle fluctuation
synergy analysis
spearman correlation analysis method
82.20.wt
88.05.lg
88.05.qr
url https://doi.org/10.1515/phys-2018-0119
work_keys_str_mv AT chengshen synergyanalysisofagriculturaleconomiccyclefluctuationbasedonantcolonyalgorithm
AT alopezmiguel synergyanalysisofagriculturaleconomiccyclefluctuationbasedonantcolonyalgorithm